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Cross-Classification Multilevel Logistic Models in Psychometrics

Author

Listed:
  • Wim Van den Noortgate
  • Paul De Boeck
  • Michel Meulders

Abstract

In IRT models, responses are explained on the basis of person and item effects. Person effects are usually defined as a random sample from a population distribution. Regular IRT models therefore can be formulated as multilevel models, including a within-person part and a between-person part. In a similar way, the effects of the items can be studied as random parameters, yielding multilevel models with a within-item part and a between-item part. The combination of a multilevel model with random person effects and one with random item effects leads to a cross-classification multilevel model, which can be of interest for IRT applications. The use of cross-classification multilevel logistic models will be illustrated with an educational measurement application.

Suggested Citation

  • Wim Van den Noortgate & Paul De Boeck & Michel Meulders, 2003. "Cross-Classification Multilevel Logistic Models in Psychometrics," Journal of Educational and Behavioral Statistics, , vol. 28(4), pages 369-386, December.
    • Handle: RePEc:sae:jedbes:v:28:y:2003:i:4:p:369-386
    DOI: 10.3102/10769986028004369
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    Cited by:

    1. Marc Callens, 2017. "Long Term Trends in Life Satisfaction, 1973–2012: Flanders in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(1), pages 107-127, January.

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